Polars to SQL: Compiling DataFrame Logic for the Warehouse

I spent three days last week debugging a pipeline where the local Python transformations didn’t match the production SQL views. It happens constantly. You write this highly readable, chainable Polars code to test your logic on a sample dataset. Then you have to rewrite the exact same logic in Postgres dialect to run it against the actual data warehouse. It’s a massive waste of time.

Polars fixed the single-node memory issues we suffered through for a decade with older libraries. I’m running Polars 1.5.2 on my M3 Max MacBook right now and it chews through 40GB parquet files without breaking a sweat. But that doesn’t help when the raw data lives in a remote warehouse and totals three terabytes. You can’t pull that over the wire. You have to push the compute down to the database.

And writing raw SQL for complex analytical queries gets ugly fast. Deeply nested CTEs pile up. Window functions stretch for eighty lines. Polars expressions just make more sense for sequential data manipulation.

The Translation Layer

Someone on my team — well, actually, let me back up. Someone on my team figured out you can feed Polars LazyFrame expressions into a large language model and ask for the equivalent SQL. I thought this was a terrible idea at first. Generating code dynamically in a data pipeline sounds like a recipe for silent data corruption.

But I was probably wrong. If you restrict the context window, provide the schema, and demand a structured output format, it works surprisingly well for translating syntax. You aren’t asking the model to invent logic. You’re asking it to act as a compiler between two highly structured declarative languages.

Here is the kind of logic we want to run in the warehouse:

import polars as pl

# The logic we want to execute remotely
query = (
    pl.scan_table("user_events")
    .filter(pl.col("event_date") >= pl.date(2026, 1, 1))
    .group_by("customer_id")
    .agg([
        pl.col("purchase_amount").sum().alias("total_spent"),
        pl.col("session_id").n_unique().alias("visit_count")
    ])
    .filter(pl.col("total_spent") > 500)
)

That takes about five seconds to write. The equivalent SQL isn’t hard, but context-switching between Python and SQL syntax breaks focus. When we pass the string representation of that Polars plan through our translation script, we get this back:

SELECT 
    customer_id,
    SUM(purchase_amount) AS total_spent,
    COUNT(DISTINCT session_id) AS visit_count
FROM user_events
WHERE event_date >= '2026-01-01'
GROUP BY customer_id
HAVING SUM(purchase_amount) > 500;

I tested this entire pipeline using Python 3.12.2. We take the AST from the Polars expression, pass it to the model to generate the SQL string, and execute that string via SQLAlchemy. Pushing the generated SQL down to the database dropped our execution time from 14 minutes (pulling data to local Polars) to just 42 seconds.

The Window Function Trap

But here is where things break. I’ve been running this setup for two months and discovered a massive blind spot in how these models handle the translation.

When you translate Polars over() expressions—their version of window functions—to SQL, the models consistently mess up the PARTITION BY framing if there are nulls in the grouping column.

Polars handles nulls in groups differently than standard SQL. And if you just let the model blindly translate a rolling average partitioned by a category column, your SQL engine might group all nulls into a single massive partition. This blows up your warehouse memory immediately.

You have to explicitly tell your translation prompt to use NULLS LAST or filter them out before the window function. I learned this the hard way after getting a rather aggressive automated email about resource consumption on our staging cluster. The query tried to sort a partition of 40 million null records on a single node.

Looking Ahead

Right now, using an AI model to translate DataFrame operations feels like a temporary hack. A highly effective one, but still a hack.

And by Q3 2027, I expect query engines to just accept Polars expressions natively. We already see DuckDB doing something similar with its relational API. There is no technical reason we need to serialize these plans into SQL text just to deserialize them back into an execution plan on the database server.

But until those native integrations mature, generating SQL from Python logic is the most pragmatic way to keep your codebase readable while actually using your database compute. Just check the generated queries for null handling before pushing them to production.